Photonic Matrix Computing: From Fundamentals to Applications

Cheng, Junwei; Zhou, Hailong; Dong, Jianji

doi:10.3390/nano11071683

Cited by 41 publications

(19 citation statements)

References 73 publications

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“…The detailed mechanism of these MVMs can be found in ref. 33 , which offers an easy-to-read overview of principle and development of photonic matrix computation. The first kind of optical MVM (PLC-MVM) is implemented by the diffraction of light in free space.…”

Section: Matrix-vector Multiplicationmentioning

confidence: 99%

Photonic matrix multiplication lights up photonic accelerator and beyond

et al. 2022

Self Cite

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Matrix computation, as a fundamental building block of information processing in science and technology, contributes most of the computational overheads in modern signal processing and artificial intelligence algorithms. Photonic accelerators are designed to accelerate specific categories of computing in the optical domain, especially matrix multiplication, to address the growing demand for computing resources and capacity. Photonic matrix multiplication has much potential to expand the domain of telecommunication, and artificial intelligence benefiting from its superior performance. Recent research in photonic matrix multiplication has flourished and may provide opportunities to develop applications that are unachievable at present by conventional electronic processors. In this review, we first introduce the methods of photonic matrix multiplication, mainly including the plane light conversion method, Mach–Zehnder interferometer method and wavelength division multiplexing method. We also summarize the developmental milestones of photonic matrix multiplication and the related applications. Then, we review their detailed advances in applications to optical signal processing and artificial neural networks in recent years. Finally, we comment on the challenges and perspectives of photonic matrix multiplication and photonic acceleration.

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Section: Matrix-vector Multiplicationmentioning

confidence: 99%

Photonic matrix multiplication lights up photonic accelerator and beyond

et al. 2022

Self Cite

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“…Several well-developed technologies can help sustain reliability within a simple structure. In particular, the weight matrices of the photonic neural networks can be directly mapped onto MRR arrays [170], loaded and get fine-tuned at high speed (~ 54 GHz [171]) through standard electrical control [172,173]. With this architecture, scaling up neural networks does not lead to a dramatic escalation in overhead since one single-mode fiber can accommodate multi-channel inputs.…”

Section: Optical Computingmentioning

confidence: 99%

Prospects and applications of on-chip lasers

Zhou

Fang

et al. 2023

eLight

137

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Integrated silicon photonics has sparked a significant ramp-up of investment in both academia and industry as a scalable, power-efficient, and eco-friendly solution. At the heart of this platform is the light source, which in itself, has been the focus of research and development extensively. This paper sheds light and conveys our perspective on the current state-of-the-art in different aspects of application-driven on-chip silicon lasers. We tackle this from two perspectives: device-level and system-wide points of view. In the former, the different routes taken in integrating on-chip lasers are explored from different material systems to the chosen integration methodologies. Then, the discussion focus is shifted towards system-wide applications that show great prospects in incorporating photonic integrated circuits (PIC) with on-chip lasers and active devices, namely, optical communications and interconnects, optical phased array-based LiDAR, sensors for chemical and biological analysis, integrated quantum technologies, and finally, optical computing. By leveraging the myriad inherent attractive features of integrated silicon photonics, this paper aims to inspire further development in incorporating PICs with on-chip lasers in, but not limited to, these applications for substantial performance gains, green solutions, and mass production.

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“…The field of optical computing was stagnant for a period of time due to severe limitations in device performance including low integration density and weak nonlinearity. In recent years, however, with the improvement of micro-nano optoelectronics technology and the huge demand for arithmetic power for AI applications, optical computing has gained more possibilities and attention, and thus become the frontier and hot spot of micro and nano optoelectronics research [9,10].…”

Section: Introductionmentioning

confidence: 99%

Optical Neural Networks for Holographic Image Recognition (Invited Paper)

Feng¹,

Niu²,

Zhang³

et al. 2023

PIER

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Inspired by neural networks based on traditional electronic circuits, optical neural networks (ONNs) show great potential in terms of computing speed and power consumption. Though some progress has been made in devices and schemes, ONNs are still a long way from replacing electronic neural networks in terms of generalizability. Here, we present a complex optical neural network (cONN) for holographic image recognition, within which a high-speed parallel operating unit for complex matrices is proposed, targeting the real-imaginary-splitting and column splitting. Based on the proposed cONN, we have numerically demonstrated the training-recognition process on our cONN for holographic images converted from handwritten digit datasets, achieving an accuracy of 90% based on the back-propagation algorithm. Our training-verification integrated architecture will enrich the further development and applications of on-chip photonic matrix computing.

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Photonic Matrix Computing: From Fundamentals to Applications

Cited by 41 publications

References 73 publications

Photonic matrix multiplication lights up photonic accelerator and beyond

Photonic matrix multiplication lights up photonic accelerator and beyond

Prospects and applications of on-chip lasers

Optical Neural Networks for Holographic Image Recognition (Invited Paper)

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